A Coherence-Based Measure of AGI
Published in Foundations of Agentic Systems Theory @ AAAI, 2026
Fares Fourati
Abstract
Recent work by Hendrycks et al. 2025 formalized Artificial General Intelligence (AGI) as the arithmetic mean of proficiencies across cognitive domains derived from the Cattell–Horn–Carroll (CHC) model of human cognition. While elegant, this definition assumes compensability – that exceptional ability in some domains can offset failure in others. True general intelligence, however, should reflect coherent sufficiency: balanced competence across all essential domains. We propose a coherence-aware measure of AGI based on the integral of generalized means over a continuum of compensability exponents. This formulation spans arithmetic, geometric, and harmonic regimes, and the resulting area under the curve (AUC) quantifies robustness under varying compensability assumptions. Unlike the arithmetic mean, which rewards specialization, the AUC penalizes imbalance and captures inter-domain dependency. Applied to published CHC-based domain scores for GPT-4 and GPT-5, the coherence-adjusted AUC reveals that both systems remain far from general competence despite high arithmetic scores (e.g., GPT-5 at~24\%). Integrating the generalized mean thus yields a principled, interpretable, and stricter foundation for measuring genuine progress toward AGI.